In the prior art, it is known to design experiments using statistical experiment design methods. Such design methods are used, inter alia, to determine, with a minimum number of experiments, an empirical process model for the relationship between controlled variables and influencing variables in a process and for the resulting product properties and process properties. Such statistical experiment design methods can be performed, for example, using the “STAVEX” (STAtistical experiment designing with EXpert system , manufacturer AICOS Technologies, Switzerland) computer software program and software sold under the name “Statistica®” by StatSoft (Europe) GmbH, Germany.
Various, different prior art experiment design techniques exist in the field of statistical experiment design. All statistical experiment design methods originate from the classic, fully factorial method. The factorial method compares all of the quality-conditioned factors with one another by analogy with variance analysis. Over the course of the last few decades, numerous variants of the factorial method have been developed and validated in research and development laboratories.
Modern experiment design methods according to Taguchi or Shainin are distinguishable from the classic, fully factorial methods. The Shainin Design of Experiment (“DOE”) method is a suitable optimization process because it isolates what are known as strong influencing variables and performs processing to determine their relevance and dependence. The Taguchi DOE is based on prior art fractional factorial, orthogonal experiment designs. As pre-selecting the most important influencing variables achieves drastic savings in terms of experiment runs necessary, the Tagauchi technique is a rapid and relatively economic method of designing experiments and processes.
Further known statistical experiment design techniques of the fractional factorial experiment design type include Plackett-Burmann experiment designs, central composite designs, Box-Behnken experiment designs, D-optimal designs, mixed designs, balanced block designs, Latin squares, and desperado designs (see e.g. Eberhard Scheffler, Statische Versuchsplanung und-Auswertung, Deutscher Verlag für Grundstoffindustrie, Stuttgart, 1997).
Additional methods for designing experiments are also known from Hans Bendemer, “Optimale Versuchsplanung” [Optimum experiment design], Reihe Deutsche Taschenbücher (DTB, Volume 23, and ISBN 3-87144-278-X) and Wilhem Kleppmann, Taschenbuch Versuchsplanung, “Produkte und Prozesse optimieren” [Optimize products and processes], 2nd expanded edition, ISBN: 3-446-21615-4. These methods are often used in practice for reasons of cost.
The disadvantage with known statistical methods for designing experiments is that the processing associated with experiment design and modelling is performed without accounting for additional knowledge. Consequently, under certain circumstances, no suitable optima are found and the reliability of the results and statements generated is questionable. A further significant disadvantage of prior art methods for designing experiments is that, where a large number of influencing variables need to be taken into account, the prior art methods become too extensive. In addition, with respect to certain experimental systems, for example in catalysis or active ingredient research, the target function is often heavily fractured and, therefore, is difficult to capture with statistical methods.
WO 00/15341, incorporated by reference herein, discloses a method for developing solid catalysts for heterogeneous catalysed reaction processes, which is based on parallelized testing according to evolutionary methods. Corresponding methods which operate in an evolutionary way are also known from WO 00/43411, J. chem. Inf. Compute. Sci. 2000, 40, 981 987 “Heterogeneous Catalyst Design Using Stochastic Optimization Algorithms” and from Applied Catalysis A: General 200 (2000) 63 77 “An evolutionary approach in the combinatorial selection and optimization of catalytic materials”, each of which incorporated by reference herein.
In addition, U.S. Pat. No. 6,009,379, incorporated by reference herein, discloses a method for controlling a manufacturing process by means of an efficient experimental design. According to this patent, test points are distributed uniformly on a multidimensional spherical surface so that the individual manufacturing parameters can be weighted uniformly.
FIG. 1 shows a block diagram of a prior art system 20 for performing screening experiments, such as may be used in the fields of catalysis and material and active ingredient research. It is to be understood that each of the functional blocks of the system 20 described below as performing data processing operations, as well as functional blocks of the systems described below and shown in the drawings as constituting embodiments of the present invention, constitutes a software module or, alternatively, a hardware module or a combined hardware/software module. In addition, each of the modules suitably contains a memory storage area, such as RAM, for storage of data and instructions for performing processing operations. Alternatively, instructions for performing processing operations can be stored in hardware in one or more of the modules.
Referring to FIG. 1, the system 20 includes a substance library module 1, such as a combinatorial library module, coupled to an experiment set-up module 2. The module 2 is coupled to an experiment data module 3 and a data-driven optimizer 4. The optimizer 4 also is coupled to the library module 1. The module 2 performs high throughput screening (“HTS”) or high speed experimentation (“HSE”) experiments. Such screening experiments typically are used for identifying active ingredients, catalysis research (homogeneous and heterogeneous), materials research and identification of optimum reaction conditions in chemical, biochemical or biotechnical systems. The optimizer 4 is a black-box optimizer which operates based on a data-driven model or on an evolutionary algorithm. The optimizer 4 does not have a priori knowledge of the structure and interactions concerning experiment design. The optimizer 4, instead, is restricted to the evaluation of the experiment data for purposes of selecting experiments stored at the combinatorial library module 1. The black-box optimizer 4 is implemented, for example, by means of genetic algorithms, evolutionary algorithms or strategies, neural networks or other data-driven model approaches which rely on stochastic or deterministic optimization structures or optimization structures which are a combination of both the former and latter.
In operation, the experiment set-up module 2 usually performs processing on a plurality of experiments. The module 2 provides experimental results in the form of a data file to the experiment data module 3. At the same time, the module 2 provides the experimental result data, or at least a portion thereof, as input data to the data-driven optimizer 4. The experiment data in the module 3 includes influencing variables, such as attributes, factors, structure features, descriptors, physical variables and properties of materials, and data relating to the effect these variables have on target variables. The optimizer 4 in performing its processing typically uses the experiment data stored in the module 3 to define an optimum search direction within the space of the target variables.
A common disadvantage of systems similar to the prior art system 20 is that a priori information cannot have an influence, or can only have a restricted influence, in the black-box optimizer 4, such that search strategies often converge slowly or converge on unsuitable suboptima. Consequently, prior art methods often are inefficient in terms of the expenditure of time and financial outlay. In addition, where experiment design techniques are based on evolutionary algorithms, there is a risk that the expenditure and outlay is higher when the optimizer is used to reach the optimum than when a rational or statistical procedure is used.
Therefore, there exists a need for a method and system for designing experiments using a computer based system which improves convergence speed and ensure convergences at a suitable optimum while also increasing the reliability of the results.